Proceedings of ICRC 2001: 612 c Copernicus Gesellschaft 2001

ICRC 2001

Detector Responses of the GRAPES III Scintillation and MuonCounters
T. Yoshikoshi1 , S. K. Gupta2 , Y. Hayashi1 , N. Ito1 , A. Jain2 , S. Kawakami1 , H. Kojima3 , D. K. Mohanty2 , T. Nonaka1 ,S. Noto1 , K.C. Ravindran2 , K. Satomi1 , K. Sivaprasad2 , H. Tanaka1 , S. C. Tonwar2 , T. Toyofuku1 , andK. Viswanathan21Department of Physics, Osaka City University, Osaka 558-8585, Japan
Tata Institute of Fundamental Research, Mumbai 400 005, India
3
Nagoya Women’s University, Nagoya 467-8610, Japan
2Abstract. Detector responses to air shower particles incident on the electron (scintillation) and muon counters of theGRAPES III experiment at Ooty have been investigated using Monte Carlo simulations based on Geant4. The results
are summarized as response functions which are then used
for simulating air shower events for reducing systematic uncertainty in primary energy estimation. The intensity spectrum of signals detected by the scintillation counter is calculated using these response functions and compared with theobserved intensity spectrum.

1Introduction

The air shower array of the GRAPES III experiment has beenin operation at Ooty, India since 1999 with the first-stage
configuration of 217 1 m2 electron (scintillation) countersand 16 560 m2 muon counters (Ito et al., 1997). The experiment aims to measure the energy spectrum and the chemical composition of cosmic rays around the knee region (justabove 1 PeV). The dense array of the scintillation counters(8 m span) and the large area muon counters are useful tomeasure them accurately. Observed results obtained using
these detectors have to be exactly compared with results of
air shower simulations to estimate above characteristics ofcosmic rays as in the recent reports from some experiments(Arqueros et al., 2000; Bernlöhr et al., 2000). In doing suchcomparisons, it is very important to understand the detector
responses to air shower particles correctly. Figure 1 showsthe energy spectra of air shower particles at the Ooty level(2200 m asl) generated using the air shower simulation program CORSIKA (Heck et al., 1998), together with the response (energy deposit) functions for one shower particle
vertically entering the scintillation counter, which are later
explained in detail in this paper. One can see that we havemore particles at lower energies down to 1 MeV, but in conCorrespondence to: T. Yoshikoshi(tyoshiko@sci.osaka-cu.ac.jp)

Fig. 1. Energy spectra of air shower particles at the Ooty level generated using the air shower simulation program CORSIKA and theresponse (energy deposit) functions for one shower particle entering
the GRAPES III scintillation counter.

trast, the detector response to shower particles rapidly decreases below about 20 MeV. Since the opposite tendencies
are competing with each other, estimated shower sizes are
fragile with respect to the detector response. One can also
see in Figure 1 that the number of secondary gamma rays
is an order of magnitude greater than that of electrons especially at low energies. Although our scintillation counters are
relatively insensitive to gamma rays because they do not haveany converters such as the counters of the Tibet (Amenomoriet al., 1990) and HEGRA (Merck et al., 1996) groups, we613
should investigate how much the contribution of secondary
gamma rays is in primary energy estimation.
As for the muon counters, the detector response is closely
related to the accuracy of the chemical composition of cosmic rays since mass numbers of primary cosmic rays areprobed by detected numbers of secondary muons. Resolutionof the mass number estimated from the muon number is notmuch better than the overall range of observed mass numbers(Weber et al., 1999) owing to the fluctuation of the air shower
development. Therefore, the fine structure of the mass number distribution determines the accuracy of the fraction ofminor components. The accuracy is also sensitive to the detector response on the same score. Our muon counters canalso detect single or multiple muon events without getting
any trigger signal from the air shower array, that is, they canbe used to monitor the intensity of lower energy cosmic rays
and to study the solar modulation of cosmic rays at lower
energies (Kawakami et al., 2001). Taking coincidences of
16 muon counters allows us to select primary cosmic rays ofdifferent energies and makes a study of the solar modulationpossible in a wide energy range using the same detector system. The amplitude of the modulation may be smaller than
0.1% and it is again important to understand the response of
the counters correctly.
To reduce systematic errors in estimating primary energies
and chemical composition of cosmic rays due to the effects
mentioned above, we have investigated detector responses to
air shower particles incident on the counters of the GRAPES
III experiment, using Monte Carlo simulations based on thedetector simulation toolkit Geant4 (Giani et al., 1998). Although the program is still under development, the results
currently obtained are summarized in this paper.

22.1

Simulations and results
Electron (scintillation) counter

The virtual scintillation counter constructed using Geant4 isvisualized and shown in Figure 2. Each of the scintillation
counters consists of four 50 cm × 50 cm × 5 cm scintillators which are put inside of a container and covered by a raincover, both of which are made of aluminium of 0.8 mm thickness. The scintillators are viewed by a 2 inch photomultipliertube (PMT) from the height of 50 cm.
Signals from each PMT are divided and sent to a discriminator and an ADC module. The discriminator has two outputs, one of which is sent to a TDC module and the other isused to generate event triggers. The gain of each scintillation
counter is monitored by using penetration of single muons,in which most of muons vertically enter the counter. TheADC value at the single muon peak is calibrated and kept tobe about 20 counts. In the configuration of 217 scintillation
counters, air shower triggers are generated by using central
96 counters. We have adopted two different trigger conditions, which are called the level-0 and level-1 triggers. Theformer is a kind of pattern trigger that requires a localized

Fig. 2. Virtual scintillation counter of the GRAPES III experimentconstructed using Geant4.

pattern of at least three detectors, and the latter requires simultaneous hits of any 9 detectors. The events that satisfy
the both conditions are recorded.
5 × 106 events of gamma rays, 2 × 106 events of electrons(and positrons) and 2×106 events of muons are generated using the simulation program. Their energies are distributed inthe region between 1 MeV and 10 GeV for gamma rays andelectrons, between 10 MeV and 100 GeV for muons withthe equal weight in the logarithmic scale. The particles are
randomly injected into the scintillator area of 1 m × 1 m
from the directions of which zenith angles are smaller than60◦ . The simulation program outputs energy deposits in thefour scintillators due to incident particles. The obtained energy deposit distribution for each particle is averaged on theincident position and the azimuth angle since it is almost independent of these parameters. The resultant mean energy
deposit distributions for gamma rays, electrons and muons
are shown in Figure 3. Each of the distributions is fit by atwo dimensional function of particle energy and zenith angle(cot θ) as shown in the figure. The residuals of the fit are
well smaller than 10% except for inclined (θ & 55◦ ) gamma
rays, for which the number of simulated events is not enough.
However, the fraction of such inclined events is negligibly
small considering the observed zenith angle distribution ofprimary cosmic rays (∝ cos7.5 θ).
We have tried to reproduce the typical intensity spectrumof signals detected by the scintillation counter, using the meanenergy deposit functions defined above. First, 1000 proton
showers are generated using CORSIKA with the QGSJET
model (Kalmykov and Ostapchenko, 1993) assuming that theirenergy spectrum has a power-law form of the index of −2.7.
Then, shower particles entering each scintillation counter arefed into the functions and resultant energy deposits are accumulated. Here we assume the intensity of scintillation lightbeing independent of the incident position of the particle and614

Fig. 4. Intensity spectra of signals detected by the scintillation
counter obtained using the full simulation program (filled circle)
and the program based on the mean energy deposit functions (opencircle), which are compared with the typical observed ADC spectrum (solid line).

Fig. 3. Mean energy deposit distributions as a function of particle energy for electrons (top), gamma rays (middle) and muons(bottom) obtained using Monte Carlo simulations based on Geant4.
Several distributions for different zenith angles (cot θ) are sampledand plotted.

The overall tendency of the full simulation spectrum agrees
with that of the observed spectrum except for the shape near
the single particle peak. This difference must come from thefact that we have ignored some fluctuations in the simulations. The spectrum obtained using the energy deposit functions is also similar to the observed spectrum, however, itgives lower triggering rates and a slightly higher value of thesingle particle peak than the full simulation spectrum. Thesedifferences are probably due to the fact that we have also ignored the fluctuation of energy deposits when we define thefunctions. We should further consider the fluctuations notincluded in the current simulations.
2.2

ignored the fluctuation of the number of photoelectrons generated in the PMT. The total energy deposit of each detector
is compared with the typical discrimination level (∼ 4 MeV)
and the hit detectors are compared with the condition of thelevel-1 trigger. For the sake of comparison, we also fed thesame shower particles into the full Monte Carlo simulation
program based on Geant4. Figure 4 shows the energy deposit spectra obtained by the above two methods and thetypical observed intensity (ADC) spectrum. The spectra arenormalized in the horizontal direction using the conversion
factor of 2 ADC counts MeV−1 assuming the typical energy deposit by a relativistic particle in the scintillator to be
2 MeV g−1 cm2 . The fluctuation corresponding to the observed pedestal distribution is added to the simulated spectra.
The spectrum of the full Monte Carlo simulation is verticallynormalized to the observed spectrum at the pedestal peak.

Muon counter

The virtual muon counters constructed using Geant4 are visualized and shown in Figure 5. We call this unit of counters ‘muon station’. One muon station consists of four muoncounter modules, each of which has four layers of 58 10 cm
× 10 cm × 6 m proportional counters sandwiched by concrete absorbers of 15 cm thickness and covered by concreteslabs of about 210 cm thickness. The proportional counters
are filled by the P-10 gas (Ar 90%, CH4 10%) of the pressureof 830 mmHg. At each detector module, we are monitoring
the rate of any three or four-fold trigger events, which are required to have any three or four coincident layers having atleast one hit counter.
2 × 105 muon events have been injected into the virtualmuon station. Incident positions are randomly distributed in
the area of the counter array and incident directions are also

615

Fig. 6. Triggering efficiencies of four-fold events detected by thevirtual muon counter as a function of muon energy and zenith angle.

3Fig. 5. Top and side views of the virtual muon station of theGRAPES III experiment constructed using Geant4.

randomized in the region of 0◦ < θ < 50◦ . The simulation
program outputs energy deposits in the P-10 gas of each proportional counters, which are compared with the typical hit
threshold level of 6 keV corresponding to about 0.2 particle
and hit counters are compared with the trigger conditions.
We have calculated the triggering efficiency that is here defined to be the ratio of the number of triggered events to thenumber of particles incident on the 6 m × 6 m area between
the second and third layers. The efficiencies are averaged
on the incident position and the azimuth angle again as thecase of the scintillation counter. Figure 6 shows the four-fold
triggering efficiency at one module as a function of energy
and zenith angle. One can see that the threshold energy formuons is slightly higher than 1 GeV, which is consistent withthe analytical estimation (Ito et al., 1997). The triggering efficiencies slightly decrease with increasing the zenith angle
since some inclined particles are out of the geometry of thelayers, and have small deficits near the vertical direction inwhich the probability of muons passing through the gaps between the counters or the counter walls without depositing
any energy in the gas is slightly higher.

Conclusions

We have constructed the virtual detectors in imitation of theGRAPES III scintillation and muon counters using Geant4
and investigated the responses of them to air shower particles. The intensity spectrum of signals observed by thescintillation counter has been reproduced using the simulation program, but at the moment, the simulated spectra do
not agree well with the observed spectrum probably owing
to the underestimate of some fluctuations and further investigation is necessary. After some adequate modifications, weare planning to use the simulation program together with theair shower simulation program so that the simulated results
reflect exact detector responses.
Acknowledgements. This research was partially supported by a Grantin-Aid for Scientific Research of the Ministry of Education, Science, Sports and Culture, Japan. We thank the staff members of theCosmic-Ray Laboratory, TIFR for the installation, operation andmaintenance work of the GRAPES III experiment. The help andcooperation of the Radio Astronomy Center, TIFR are gratefully
acknowledged for providing site facilities of the experiment.

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